Uniaxial Compressive Strength (UCS) is the most important rock parameter required and determined for rock mechanical studies in most civil and mining projects. In this study, two soft computing approaches, which are known as neuro-fuzzy inference system (ANFIS) and Genetic Programming (GP), are used in strength prediction of uniaxial compressive strength (UCS). Block Punch Index (BPI), porosity (n), P-wave velocity (Vp), Density ( ) were used as inputs for both methods and were analyzed to obtain training and testing data. Of all 130 data sets, the training and testing sets consisted of randomly selected 110 and 20 sets, respectively. Results showed that the ANFIS and GP models are capable of accurately predicting the uniaxial compressive strength (UCS) used in the training and testing phase of the study. The GP model results better prediction compared to ANFIS model.